





"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      if isinstance(SCREAMING_SNAKE_CASE				,		(list, tuple)     ) and isinstance(videos[0]				,		(list, tuple)     ) and is_valid_image(videos[0][0]     ):
            return videos

      elif isinstance(SCREAMING_SNAKE_CASE				,		(list, tuple)     ) and is_valid_image(videos[0]     ):
            return [videos]

      elif is_valid_image(SCREAMING_SNAKE_CASE     ):
            return [[videos]]

      raise ValueError(F'Could not make batched video from {videos}'     )






class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = ['pixel_values']





      def __init__(			self ,		lowercase = True ,		lowercase = None ,		lowercase = PILImageResampling.BILINEAR ,		lowercase = True ,		lowercase = None ,		lowercase = True ,		lowercase = 1 / 255 ,		lowercase = True ,		lowercase = None ,		lowercase = None ,		**lowercase ,		)						->	None:
            super().__init__(**lowercase					)
            lowerCAmelCase    		=			size if size is not None else {"""shortest_edge""": 224}
            lowerCAmelCase    		=			get_size_dict(lowercase ,		default_to_square=lowercase					)
            lowerCAmelCase    		=			crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
            lowerCAmelCase    		=			get_size_dict(lowercase ,		param_name="""crop_size"""					)

            lowerCAmelCase    		=			do_resize
            lowerCAmelCase    		=			size
            lowerCAmelCase    		=			do_center_crop
            lowerCAmelCase    		=			crop_size
            lowerCAmelCase    		=			resample
            lowerCAmelCase    		=			do_rescale
            lowerCAmelCase    		=			rescale_factor
            lowerCAmelCase    		=			do_normalize
            lowerCAmelCase    		=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            lowerCAmelCase    		=			image_std if image_std is not None else IMAGENET_STANDARD_STD





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase = PILImageResampling.BILINEAR ,		lowercase = None ,		**lowercase ,		)						->	np.ndarray:
            lowerCAmelCase    		=			get_size_dict(lowercase ,		default_to_square=lowercase					)
            if "shortest_edge" in size:
                  lowerCAmelCase    		=			get_resize_output_image_size(lowercase ,		size["""shortest_edge"""] ,		default_to_square=lowercase					)
            elif "height" in size and "width" in size:
                  lowerCAmelCase    		=			(size["""height"""], size["""width"""])
            else:
                  raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'					)
            return resize(lowercase ,		size=lowercase ,		resample=lowercase ,		data_format=lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase = None ,		**lowercase ,		)						->	np.ndarray:
            lowerCAmelCase    		=			get_size_dict(lowercase					)
            if "height" not in size or "width" not in size:
                  raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'					)
            return center_crop(lowercase ,		size=(size["""height"""], size["""width"""]) ,		data_format=lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase = None ,		**lowercase ,		)						->	List[str]:
            return rescale(lowercase ,		scale=lowercase ,		data_format=lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase = None ,		**lowercase ,		)						->	np.ndarray:
            return normalize(lowercase ,		mean=lowercase ,		std=lowercase ,		data_format=lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = ChannelDimension.FIRST ,		)						->	np.ndarray:
            if do_resize and size is None or resample is None:
                  raise ValueError("""Size and resample must be specified if do_resize is True."""					)

            if do_center_crop and crop_size is None:
                  raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

            if do_rescale and rescale_factor is None:
                  raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

            if do_normalize and (image_mean is None or image_std is None):
                  raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

            # All transformations expect numpy arrays.
            lowerCAmelCase    		=			to_numpy_array(lowercase					)

            if do_resize:
                  lowerCAmelCase    		=			self.resize(image=lowercase ,		size=lowercase ,		resample=lowercase					)

            if do_center_crop:
                  lowerCAmelCase    		=			self.center_crop(lowercase ,		size=lowercase					)

            if do_rescale:
                  lowerCAmelCase    		=			self.rescale(image=lowercase ,		scale=lowercase					)

            if do_normalize:
                  lowerCAmelCase    		=			self.normalize(image=lowercase ,		mean=lowercase ,		std=lowercase					)

            lowerCAmelCase    		=			to_channel_dimension_format(lowercase ,		lowercase					)
            return image





      def        _snake_case     (			self ,		lowercase ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = ChannelDimension.FIRST ,		**lowercase ,		)						->	PIL.Image.Image:
            lowerCAmelCase    		=			do_resize if do_resize is not None else self.do_resize
            lowerCAmelCase    		=			resample if resample is not None else self.resample
            lowerCAmelCase    		=			do_center_crop if do_center_crop is not None else self.do_center_crop
            lowerCAmelCase    		=			do_rescale if do_rescale is not None else self.do_rescale
            lowerCAmelCase    		=			rescale_factor if rescale_factor is not None else self.rescale_factor
            lowerCAmelCase    		=			do_normalize if do_normalize is not None else self.do_normalize
            lowerCAmelCase    		=			image_mean if image_mean is not None else self.image_mean
            lowerCAmelCase    		=			image_std if image_std is not None else self.image_std

            lowerCAmelCase    		=			size if size is not None else self.size
            lowerCAmelCase    		=			get_size_dict(lowercase ,		default_to_square=lowercase					)
            lowerCAmelCase    		=			crop_size if crop_size is not None else self.crop_size
            lowerCAmelCase    		=			get_size_dict(lowercase ,		param_name="""crop_size"""					)

            if not valid_images(lowercase					):
                  raise ValueError(
                      """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                      """torch.Tensor, tf.Tensor or jax.ndarray."""					)

            lowerCAmelCase    		=			make_batched(lowercase					)

            lowerCAmelCase    		=			[
                [
                    self._preprocess_image(
                        image=lowercase ,		do_resize=lowercase ,		size=lowercase ,		resample=lowercase ,		do_center_crop=lowercase ,		crop_size=lowercase ,		do_rescale=lowercase ,		rescale_factor=lowercase ,		do_normalize=lowercase ,		image_mean=lowercase ,		image_std=lowercase ,		data_format=lowercase ,		)
                    for img in video
                ]
                for video in videos
            ]

            lowerCAmelCase    		=			{"""pixel_values""": videos}
            return BatchFeature(data=lowercase ,		tensor_type=lowercase					)













"""simple docstring"""





import logging
import os

from .state import PartialState

class 						lowercase   (   logging.LoggerAdapter   ):





      @staticmethod
      def        _snake_case     (			lowercase					)						->	Optional[Any]:
            lowerCAmelCase    		=			PartialState()
            return not main_process_only or (main_process_only and state.is_main_process)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		*lowercase ,		**lowercase					)						->	Dict:
            if PartialState._shared_state == {}:
                  raise RuntimeError(
                      """You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility."""					)
            lowerCAmelCase    		=			kwargs.pop("""main_process_only""" ,		lowercase					)
            lowerCAmelCase    		=			kwargs.pop("""in_order""" ,		lowercase					)

            if self.isEnabledFor(lowercase					):
                  if self._should_log(lowercase					):
                        lowerCAmelCase		,						lowerCAmelCase    		=			self.process(lowercase ,		lowercase					)
                        self.logger.log(lowercase ,		lowercase ,		*lowercase ,		**lowercase					)

                  elif in_order:
                        lowerCAmelCase    		=			PartialState()
                        for i in range(state.num_processes					):
                              if i == state.process_index:
                                    lowerCAmelCase		,						lowerCAmelCase    		=			self.process(lowercase ,		lowercase					)
                                    self.logger.log(lowercase ,		lowercase ,		*lowercase ,		**lowercase					)
                              state.wait_for_everyone()







def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		str = None     ):







      '''simple docstring'''




      if log_level is None:
            lowerCAmelCase    		=			os.environ.get("""ACCELERATE_LOG_LEVEL"""				,		SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			logging.getLogger(SCREAMING_SNAKE_CASE     )
      if log_level is not None:
            logger.setLevel(log_level.upper()     )
            logger.root.setLevel(log_level.upper()     )
      return MultiProcessAdapter(SCREAMING_SNAKE_CASE				,		{}     )






